{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "00_Keras_RNN_predictions_solution.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wxmLPc2YysH",
        "colab_type": "text"
      },
      "source": [
        "# An RNN for short-term predictions\n",
        "This model will try to predict the next value in a short sequence based on historical data. This can be used for example to forecast demand based on a couple of weeks of sales data.\n",
        "\n",
        "This is the solution notebook. The corresponding work notebook is here: [00_Keras_RNN_predictions_playground.ipynb](https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rnn-tutorial/00_Keras_RNN_predictions_playground.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uk3AMTcYysJ",
        "colab_type": "code",
        "outputId": "cdf5c384-f022-423f-e55d-3b5741b69010",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# using Tensorflow 2\n",
        "%tensorflow_version 2.x\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow as tf\n",
        "print(\"Tensorflow version: \" + tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "Tensorflow version: 2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk3oZyKsZLRf",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Display utilities [RUN ME]\n",
        "\n",
        "from enum import IntEnum\n",
        "import numpy as np\n",
        "\n",
        "class Waveforms(IntEnum):\n",
        "    SINE1 = 0\n",
        "    SINE2 = 1\n",
        "    SINE3 = 2\n",
        "    SINE4 = 3\n",
        "\n",
        "def create_time_series(waveform, datalen):\n",
        "    # Generates a sequence of length datalen\n",
        "    # There are three available waveforms in the Waveforms enum\n",
        "    # good waveforms\n",
        "    frequencies = [(0.2, 0.15), (0.35, 0.3), (0.6, 0.55), (0.4, 0.25)]\n",
        "    freq1, freq2 = frequencies[waveform]\n",
        "    noise = [np.random.random()*0.2 for i in range(datalen)]\n",
        "    x1 = np.sin(np.arange(0,datalen) * freq1)  + noise\n",
        "    x2 = np.sin(np.arange(0,datalen) * freq2)  + noise\n",
        "    x = x1 + x2\n",
        "    return x.astype(np.float32)\n",
        "\n",
        "from matplotlib import transforms as plttrans\n",
        "\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "\n",
        "def picture_this_1(data, datalen):\n",
        "    plt.subplot(211)\n",
        "    plt.plot(data[datalen-512:datalen+512])\n",
        "    plt.axvspan(0, 512, color='black', alpha=0.06)\n",
        "    plt.axvspan(512, 1024, color='grey', alpha=0.04)\n",
        "    plt.subplot(212)\n",
        "    plt.plot(data[3*datalen-512:3*datalen+512])\n",
        "    plt.axvspan(0, 512, color='grey', alpha=0.04)\n",
        "    plt.axvspan(512, 1024, color='black', alpha=0.06)\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_2(data, batchsize, seqlen):\n",
        "    samples = np.reshape(data, [-1, batchsize, seqlen])\n",
        "    rndsample = samples[np.random.choice(samples.shape[0], 8, replace=False)]\n",
        "    print(\"Tensor shape of a batch of training sequences: \" + str(rndsample[0].shape))\n",
        "    print(\"Random excerpt:\")\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        plt.plot(rndsample[i, 0]) # first sequence in random batch\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_3(predictions, evaldata, evallabels, seqlen):\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        #k = int(np.random.rand() * evaldata.shape[0])\n",
        "        l0, = plt.plot(evaldata[i, 1:], label=\"data\")\n",
        "        plt.plot([seqlen-2, seqlen-1], evallabels[i, -2:], \":\")\n",
        "        l1, = plt.plot([seqlen-1], [predictions[i]], \"o\", label='Predicted')\n",
        "        l2, = plt.plot([seqlen-1], [evallabels[i][-1]], \"o\", label='Ground Truth')\n",
        "        if i==0:\n",
        "            plt.legend(handles=[l0, l1, l2])\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def histogram_helper(data, title, last_label=None):\n",
        "  labels = ['RND', 'LAST', 'LAST2', 'LINEAR', 'DNN', 'CNN', 'RNN', 'RNN_N']\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334',\n",
        "            '#4285f4', '#34a853', '#fbbc05', '#ea4334',\n",
        "            '#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  fig = plt.figure(figsize=(7,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  ymax = data[1]*1.3\n",
        "  plt.ylim(0, ymax)\n",
        "  plt.title(title, pad=\"20\")\n",
        "  # remove data points where data is None\n",
        "  filtered = filter(lambda tup: tup[1] is not None, zip(labels, data, colors))\n",
        "  # split back into lists\n",
        "  labels, data, colors = map(list, zip(*filtered))\n",
        "  # replace last label is appropriate\n",
        "  if last_label is not None:\n",
        "    labels[-1] = last_label\n",
        "  # histogram plot\n",
        "  plt.bar(labels, data, color=colors)\n",
        "  # add values on histogram bars\n",
        "  for i, (_, v, color) in enumerate(zip(labels, data, colors)):\n",
        "      plt.gca().text(i-0.3, min(v, ymax)+0.02, \"{0:.4f}\".format(v), color=color, fontweight=\"bold\")\n",
        "  plt.show()\n",
        "\n",
        "def picture_this_hist_yours(data):\n",
        "  histogram_helper(data, 'RMSE: your model vs. other approaches',\n",
        "                   last_label='Yours')\n",
        "\n",
        "def picture_this_hist_all(data):\n",
        "  histogram_helper(data, 'RMSE: final comparison')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJ-1YaKQYysO",
        "colab_type": "text"
      },
      "source": [
        "## Generate fake dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAhYNwMUYysP",
        "colab_type": "code",
        "outputId": "eccefb0e-77be-4c22-8635-1ab02e3e5489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "DATA_SEQ_LEN = 1024*128\n",
        "data = np.concatenate([create_time_series(waveform, DATA_SEQ_LEN) for waveform in Waveforms]) # 4 different wave forms\n",
        "picture_this_1(data, DATA_SEQ_LEN)\n",
        "DATA_LEN = DATA_SEQ_LEN * 4 # since we concatenated 4 sequences"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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WIAoB8B03XDzEswqzA61V3bUBz2aTVR1LeQlvvmkSLz/c7FUOcBzGYyHPmeNrBcMw8O+n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\nRJuryFbK8TAce7lKr7PCSsOAH5CehdVmNthj6aT05dwysz3MAAl0J1UUbbb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eQ5LA7VGd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\ncH1iqzVvY/RO4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4FNwBvl+GhgT2YOluORsfdmXJbzr5brpcm2CHgFuLNMF7gtImZjzlSN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\nWZIkSZKkFiyYJUmSJElq4b/uCR3BU0aukgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yt3TIQ6EYysS",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNw-tcLPYysS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RNN_CELLSIZE = 32   # size of the RNN cells\n",
        "SEQLEN = 16         # unrolled sequence length\n",
        "BATCHSIZE = 32      # mini-batch size\n",
        "LAST_N = SEQLEN//2  # loss computed on last N element of sequence in advanced RNN model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzLvRzgjYysU",
        "colab_type": "text"
      },
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxTQ2-BRYysV",
        "colab_type": "code",
        "outputId": "65f4ae1a-7bc3-4227-d15d-d40811fe92dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "picture_this_2(data, BATCHSIZE, SEQLEN) # execute multiple times to see different sample sequences"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor shape of a batch of training sequences: (32, 16)\n",
            "Random excerpt:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7nb8WgHO3FdEpIoDkyECrZfPxdOfSntEs2llCTX0jGUWV3PjJesICvfnytgGE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\ns1BKeXFsmcWc3x2TD4wEUEq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Z6wa2o7D8KMt3SwtvO9GcFpF2yWzRPPX9TqKCfLjngk5G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\ngg==\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idWk7K3FYysn",
        "colab_type": "text"
      },
      "source": [
        "## Prepare datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOmIaCtYyso",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# training to predict the same sequence shifted by one (next value)\n",
        "labeldata = np.roll(data, -1)\n",
        "\n",
        "# cut data into sequences\n",
        "traindata = np.reshape(data, [-1, SEQLEN])\n",
        "labeldata = np.reshape(labeldata, [-1, SEQLEN])\n",
        "\n",
        "# make an evaluation dataset by cutting the sequences differently\n",
        "evaldata = np.roll(data, -SEQLEN//2)\n",
        "evallabels = np.roll(evaldata, -1)\n",
        "evaldata = np.reshape(evaldata, [-1, SEQLEN])\n",
        "evallabels = np.reshape(evallabels, [-1, SEQLEN])\n",
        "\n",
        "def get_training_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          traindata, # features\n",
        "          labeldata[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.repeat()\n",
        "  dataset = dataset.shuffle(DATA_LEN//SEQLEN) # shuffling is important ! (Number of sequences in shuffle buffer: all of them)\n",
        "  dataset = dataset.batch(BATCHSIZE, drop_remainder = True)\n",
        "  return dataset\n",
        "\n",
        "def get_evaluation_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          evaldata, # features       \n",
        "          evallabels[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.batch(evaldata.shape[0], drop_remainder = True) # just one batch with everything\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q443hxmJ5LNe",
        "colab_type": "text"
      },
      "source": [
        "### Peek at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN027ePq5Kxy",
        "colab_type": "code",
        "outputId": "87070a1c-000c-4b42-f52e-2ca13f3102d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train_ds = get_training_dataset()\n",
        "for features, labels in train_ds.take(10):\n",
        "  print(\"input_shape:\", features.numpy().shape, \", shape of labels:\", labels.numpy().shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGh2-0E6YysZ",
        "colab_type": "text"
      },
      "source": [
        "## Benchmark model definitions\n",
        "We will compare the RNNs against these models. For the time being you can regard them as black boxes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVNz_D8bd8lA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c619e339-a05c-4e81-a37a-d736d912dc39"
      },
      "source": [
        "# this is how to create a Keras model from neural network layers\n",
        "def compile_keras_sequential_model(list_of_layers, model_name):\n",
        "  \n",
        "    # a tf.keras.Sequential model is a sequence of layers\n",
        "    model = tf.keras.Sequential(list_of_layers, name=model_name)    \n",
        "    \n",
        "    # to finalize the model, specify the loss, the optimizer and metrics\n",
        "    model.compile(\n",
        "       loss = 'mean_squared_error',\n",
        "       optimizer = 'rmsprop',\n",
        "       metrics = ['RootMeanSquaredError'])\n",
        "    \n",
        "    # this prints a description of the model\n",
        "    model.summary()\n",
        "    \n",
        "    return model\n",
        "  \n",
        "#\n",
        "# three very simplistic \"models\" that require no training. Can you beat them ?\n",
        "#\n",
        "\n",
        "# SIMPLISTIC BENCHMARK MODEL 1\n",
        "predict_same_as_last_value = lambda x: x[:,-1] # shape of x is [BATCHSIZE,SEQLEN]\n",
        "# SIMPLISTIC BENCHMARK MODEL 2\n",
        "predict_trend_from_last_two_values = lambda x: x[:,-1] + (x[:,-1] - x[:,-2])\n",
        "# SIMPLISTIC BENCHMARK MODEL 3\n",
        "predict_random_value = lambda x: tf.random.uniform(tf.shape(x)[0:1], -2.0, 2.0)\n",
        "\n",
        "def model_layers_from_lambda(lambda_fn, input_shape, output_shape):\n",
        "  return [tf.keras.layers.Lambda(lambda_fn, input_shape=input_shape),\n",
        "          tf.keras.layers.Reshape(output_shape)]\n",
        "\n",
        "model_layers_RAND  = model_layers_from_lambda(predict_random_value,               input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST  = model_layers_from_lambda(predict_same_as_last_value,         input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST2 = model_layers_from_lambda(predict_trend_from_last_two_values, input_shape=[SEQLEN,], output_shape=[1,])\n",
        "\n",
        "#\n",
        "# three neural network models for comparison, in increasing order of complexity\n",
        "#\n",
        "\n",
        "# BENCHMARK MODEL 4: linear model (RMSE: 0.215 after 10 epochs)\n",
        "model_layers_LINEAR = [tf.keras.layers.Dense(1, input_shape=[SEQLEN,])] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 5: 2-layer dense model (RMSE: 0.197 after 10 epochs)\n",
        "model_layers_DNN = [tf.keras.layers.Dense(SEQLEN//2, activation='relu', input_shape=[SEQLEN,]), # input  shape [BATCHSIZE, SEQLEN]\n",
        "                    tf.keras.layers.Dense(1)] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 6: convolutional (RMSE: 0.186 after 10 epochs)\n",
        "model_layers_CNN = [\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for conv model\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=4, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.Conv1D(filters=16, kernel_size=3, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=1, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=3, activation='relu', padding=\"same\"),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    tf.keras.layers.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//4, 8]\n",
        "    # mis-using a conv layer as linear regression :-)\n",
        "    tf.keras.layers.Conv1D(filters=1, kernel_size=SEQLEN//4, activation=None, padding=\"valid\"), # output shape [BATCHSIZE, 1, 1]\n",
        "    tf.keras.layers.Reshape([1,]) ] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# instantiate the benchmark models and train those that need training\n",
        "steps_per_epoch = steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "NB_BENCHMARK_EPOCHS = 10\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\") # Simplistic model without parameters. It needs no training.\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_LINEAR.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "model_DNN = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_DNN.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "model_CNN = compile_keras_sequential_model(model_layers_CNN, \"CNN\")\n",
        "model_CNN.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "\n",
        "# evaluate the benchmark models\n",
        "benchmark_models = [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN]\n",
        "benchmark_rmses = []\n",
        "for model in benchmark_models:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  benchmark_rmses.append(rmse)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"RAND\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda (Lambda)              (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"LAST\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda_1 (Lambda)            (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"LAST2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda_2 (Lambda)            (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape_2 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"LINEAR\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 17\n",
            "Trainable params: 17\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.6609 - RootMeanSquaredError: 0.8129\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0770 - RootMeanSquaredError: 0.2775\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0586 - RootMeanSquaredError: 0.2421\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0519 - RootMeanSquaredError: 0.2278\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0492 - RootMeanSquaredError: 0.2219\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0481 - RootMeanSquaredError: 0.2194\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0474 - RootMeanSquaredError: 0.2177\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0475 - RootMeanSquaredError: 0.2180\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0466 - RootMeanSquaredError: 0.2159\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0473 - RootMeanSquaredError: 0.2174\n",
            "Model: \"DNN\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 145\n",
            "Trainable params: 145\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.1960 - RootMeanSquaredError: 0.4427\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0547 - RootMeanSquaredError: 0.2340\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0478 - RootMeanSquaredError: 0.2186\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0450 - RootMeanSquaredError: 0.2122\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0435 - RootMeanSquaredError: 0.2086\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0432 - RootMeanSquaredError: 0.2078\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0416 - RootMeanSquaredError: 0.2039\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0409 - RootMeanSquaredError: 0.2022\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0397 - RootMeanSquaredError: 0.1994\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 2s 2ms/step - loss: 0.0394 - RootMeanSquaredError: 0.1984\n",
            "Model: \"CNN\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_3 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 16, 8)             40        \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 16, 16)            400       \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 16, 8)             136       \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 8, 8)              0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 8, 8)              200       \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 4, 8)              0         \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 1, 1)              33        \n",
            "_________________________________________________________________\n",
            "reshape_4 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 809\n",
            "Trainable params: 809\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 4s 4ms/step - loss: 0.1119 - RootMeanSquaredError: 0.3346\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0449 - RootMeanSquaredError: 0.2118\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0414 - RootMeanSquaredError: 0.2034\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0394 - RootMeanSquaredError: 0.1984\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0389 - RootMeanSquaredError: 0.1973\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0375 - RootMeanSquaredError: 0.1937\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0368 - RootMeanSquaredError: 0.1917\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0365 - RootMeanSquaredError: 0.1911\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0359 - RootMeanSquaredError: 0.1896\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 3s 3ms/step - loss: 0.0355 - RootMeanSquaredError: 0.1884\n",
            "1/1 [==============================] - 0s 119ms/step - loss: 2.3955 - RootMeanSquaredError: 1.5477\n",
            "1/1 [==============================] - 0s 271ms/step - loss: 0.1690 - RootMeanSquaredError: 0.4111\n",
            "1/1 [==============================] - 0s 123ms/step - loss: 0.1124 - RootMeanSquaredError: 0.3352\n",
            "1/1 [==============================] - 0s 111ms/step - loss: 0.0462 - RootMeanSquaredError: 0.2150\n",
            "1/1 [==============================] - 0s 113ms/step - loss: 0.0378 - RootMeanSquaredError: 0.1944\n",
            "1/1 [==============================] - 0s 341ms/step - loss: 0.0339 - RootMeanSquaredError: 0.1841\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuNpxRbP_QYQ",
        "colab_type": "text"
      },
      "source": [
        "## RNN models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEyJklu-Yysi",
        "colab_type": "text"
      },
      "source": [
        "![deep RNN schematic](https://googlecloudplatform.github.io/tensorflow-without-a-phd/docs/images/RNN1.svg)\n",
        "<div style=\"text-align: right; font-family: monospace\">\n",
        "  X shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  Y shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  H shape [BATCHSIZE, RNN_CELLSIZE*N_LAYERS]\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPsQiS8pYysi",
        "colab_type": "code",
        "outputId": "8d2ffb50-5439-46a3-e77c-6a7d8d6d70c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# RNN model (RMSE: 0.164 after 10 epochs)\n",
        "model_RNN = tf.keras.Sequential([\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True),  # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE), # keep only last output in sequence: output shape [BATCHSIZE, RNN_CELLSIZE]\n",
        "    tf.keras.layers.Dense(1) # output shape [BATCHSIZE, 1]\n",
        "])\n",
        "\n",
        "model_RNN.compile(\n",
        "    loss = 'mean_squared_error',\n",
        "    optimizer = 'rmsprop',\n",
        "    metrics = ['RootMeanSquaredError'])\n",
        "\n",
        "model_RNN.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_5 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru (GRU)                    (None, 16, 32)            3360      \n",
            "_________________________________________________________________\n",
            "gru_1 (GRU)                  (None, 32)                6336      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 9,729\n",
            "Trainable params: 9,729\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5Kp1zjx4dY1",
        "colab_type": "code",
        "outputId": "ad2bf4ae-c257-445f-9847-a7fb150dd58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "# RNN model with loss computed on last N elements (RMSE: 0.163 after 10 epochs)\n",
        "model_RNN_N = tf.keras.Sequential([\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True),\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True), # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1)),              # output shape [BATCHSIZE, SEQLEN, 1]\n",
        "    tf.keras.layers.Lambda(lambda x: x[:,-LAST_N:SEQLEN,0]) # last N item(s) in sequence: output shape [BATCHSIZE, LAST_N]\n",
        "])\n",
        "\n",
        "model_RNN_N.compile(\n",
        "    loss = 'mean_squared_error',\n",
        "    optimizer = 'rmsprop',\n",
        "    metrics = ['RootMeanSquaredError'])\n",
        "\n",
        "model_RNN_N.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_7 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru_4 (GRU)                  (None, 16, 32)            3360      \n",
            "_________________________________________________________________\n",
            "gru_5 (GRU)                  (None, 16, 32)            6336      \n",
            "_________________________________________________________________\n",
            "time_distributed (TimeDistri (None, 16, 1)             33        \n",
            "_________________________________________________________________\n",
            "lambda_3 (Lambda)            (None, 8)                 0         \n",
            "=================================================================\n",
            "Total params: 9,729\n",
            "Trainable params: 9,729\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKoWDXEC_InP",
        "colab_type": "text"
      },
      "source": [
        "## Training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtqXxnUHhTHi",
        "colab_type": "code",
        "outputId": "13e11476-8910-401c-f47d-45498518b141",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "# You can re-execute this cell to continue training\n",
        "\n",
        "steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "NB_EPOCHS = 10      # use NB_EPOCHS=1 when coding your models\n",
        "                   # use NB_EPOCHS=10 when training for real (benchmark models were trained for 10 epochs)\n",
        "\n",
        "model = model_RNN_N # train your model: model_RNN or model_RNN_N\n",
        "train_ds = get_training_dataset(last_n=LAST_N) # use last_n=LAST_N for model_RNN_N\n",
        "\n",
        "history = model.fit(train_ds, steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 17s 16ms/step - loss: 0.0704 - RootMeanSquaredError: 0.2653\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 14s 13ms/step - loss: 0.0386 - RootMeanSquaredError: 0.1966\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0367 - RootMeanSquaredError: 0.1915\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0345 - RootMeanSquaredError: 0.1858\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0329 - RootMeanSquaredError: 0.1815\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0315 - RootMeanSquaredError: 0.1773\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0303 - RootMeanSquaredError: 0.1739\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 14s 13ms/step - loss: 0.0287 - RootMeanSquaredError: 0.1695\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0276 - RootMeanSquaredError: 0.1662\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0265 - RootMeanSquaredError: 0.1627\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "br7nVK9ijnCU",
        "colab_type": "code",
        "outputId": "34c09a3a-38bc-41b7-b09e-82248e2f698f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lylqADPDK1rqIPn/LTn3rqT49+upZSVIsHNCuplp1NkVLH5uj2loXZrEyAAAA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\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9RkMeEXW_qQ",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyOzPv8Znu61",
        "colab_type": "code",
        "outputId": "1c492af0-82a1-43c2-b190-faddbe6461f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "source": [
        "# Here \"evaluating\" using the training dataset\n",
        "eval_ds = get_evaluation_dataset(last_n=LAST_N)  # use last_n=LAST_N for model_RNN_N\n",
        "loss, your_rmse = model.evaluate(eval_ds, steps=1)\n",
        "\n",
        "# NOTE: benchmark models were trained for 10 epochs\n",
        "\n",
        "picture_this_hist_yours(benchmark_rmses + [your_rmse])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r1/1 [==============================] - 2s 2s/step - loss: 0.0287 - RootMeanSquaredError: 0.1696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DALQ65EjyijdlxdPjqXjpOcjLo3Cn3Sg681wACgYMos3ZF7D8vjso+/h/FO6y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\n",
            "text/plain": [
              "<Figure size 504x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJeFuhNRXCmq",
        "colab_type": "text"
      },
      "source": [
        "## Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpewCYd2Yys3",
        "colab_type": "code",
        "outputId": "c0a00e49-fe35-4482-eea0-4a83bd215557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "# execute multiple times to see different sample sequences\n",
        "subset = np.random.choice(DATA_LEN//SEQLEN, 8) # pick 8 eval sequences at random\n",
        "\n",
        "predictions = model.predict(evaldata[subset], steps=1) # prediction directly from numpy array\n",
        "picture_this_3(predictions[:,-1], evaldata[subset], evallabels[subset], SEQLEN)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V+StXcnjUFRzo2o3Do64gf+XKBj3f+vXr8fDw4J577jmzLSIigj//+c8ALFy4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\nnSLkt78d6BcbiI+H2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+0052yKgpwc3E/tpzYwAcyXLyxprZtkklTbsRFdQvlyXwGZRWdp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\nIVro/wNwWtPk9PhE8gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ScB9vM_Yys6",
        "colab_type": "text"
      },
      "source": [
        "<a name=\"benchmark\"></a>\n",
        "## Benchmark\n",
        "Benchmark all the algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EUH_ZsZKh1D",
        "colab_type": "code",
        "outputId": "7ce1c6fb-5fdb-444c-822a-009eda269dc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "your_RNN_layers = [\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True),  # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE), # keep only last output in sequence: output shape [BATCHSIZE, RNN_CELLSIZE]\n",
        "    tf.keras.layers.Dense(1) # output shape [BATCHSIZE, 1]\n",
        "]\n",
        "assert len(your_RNN_layers)>0, \"the model has no layers\"\n",
        "your_RNN_model = compile_keras_sequential_model(your_RNN_layers, 'RNN')\n",
        "\n",
        "your_RNN_N_layers = [\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True),\n",
        "    tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True), # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1)), # output shape [BATCHSIZE, SEQLEN, 1]\n",
        "    tf.keras.layers.Lambda(lambda x: x[:,-LAST_N:SEQLEN,0]) # last N item(s) in sequence: output shape [BATCHSIZE, LAST_N]\n",
        "]\n",
        "assert len(your_RNN_layers)>0, \"the model has no layers\"\n",
        "your_RNN_N_model = compile_keras_sequential_model(your_RNN_N_layers, 'RNN_N')\n",
        "\n",
        "# train your models from scratch\n",
        "your_RNN_model.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "your_RNN_N_model.fit(get_training_dataset(last_n=LAST_N), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "\n",
        "# evaluate all models\n",
        "rmses = []\n",
        "benchmark_models = [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN]\n",
        "for model in benchmark_models:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  rmses.append(rmse)\n",
        "_, rmse = your_RNN_model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "rmses.append(rmse)\n",
        "_, rmse = your_RNN_N_model.evaluate(get_evaluation_dataset(last_n=LAST_N), steps=1)\n",
        "rmses.append(rmse)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"RNN\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_10 (Reshape)         (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru_10 (GRU)                 (None, 16, 32)            3360      \n",
            "_________________________________________________________________\n",
            "gru_11 (GRU)                 (None, 32)                6336      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 9,729\n",
            "Trainable params: 9,729\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"RNN_N\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_11 (Reshape)         (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru_12 (GRU)                 (None, 16, 32)            3360      \n",
            "_________________________________________________________________\n",
            "gru_13 (GRU)                 (None, 16, 32)            6336      \n",
            "_________________________________________________________________\n",
            "time_distributed_2 (TimeDist (None, 16, 1)             33        \n",
            "_________________________________________________________________\n",
            "lambda_5 (Lambda)            (None, 8)                 0         \n",
            "=================================================================\n",
            "Total params: 9,729\n",
            "Trainable params: 9,729\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 17s 16ms/step - loss: 0.0746 - RootMeanSquaredError: 0.2731\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0384 - RootMeanSquaredError: 0.1959\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0352 - RootMeanSquaredError: 0.1876\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0336 - RootMeanSquaredError: 0.1833\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0329 - RootMeanSquaredError: 0.1813\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0316 - RootMeanSquaredError: 0.1778\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0304 - RootMeanSquaredError: 0.1743\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0294 - RootMeanSquaredError: 0.1713\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0285 - RootMeanSquaredError: 0.1689\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0282 - RootMeanSquaredError: 0.1679\n",
            "Train for 1024 steps\n",
            "Epoch 1/10\n",
            "1024/1024 [==============================] - 17s 17ms/step - loss: 0.0664 - RootMeanSquaredError: 0.2578\n",
            "Epoch 2/10\n",
            "1024/1024 [==============================] - 14s 13ms/step - loss: 0.0387 - RootMeanSquaredError: 0.1968\n",
            "Epoch 3/10\n",
            "1024/1024 [==============================] - 13s 13ms/step - loss: 0.0366 - RootMeanSquaredError: 0.1912\n",
            "Epoch 4/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0342 - RootMeanSquaredError: 0.1850\n",
            "Epoch 5/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0322 - RootMeanSquaredError: 0.1796\n",
            "Epoch 6/10\n",
            "1024/1024 [==============================] - 14s 13ms/step - loss: 0.0305 - RootMeanSquaredError: 0.1747\n",
            "Epoch 7/10\n",
            "1024/1024 [==============================] - 14s 13ms/step - loss: 0.0292 - RootMeanSquaredError: 0.1710\n",
            "Epoch 8/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0282 - RootMeanSquaredError: 0.1679\n",
            "Epoch 9/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0273 - RootMeanSquaredError: 0.1651\n",
            "Epoch 10/10\n",
            "1024/1024 [==============================] - 14s 14ms/step - loss: 0.0267 - RootMeanSquaredError: 0.1633\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 2.3761 - RootMeanSquaredError: 1.5415\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.1690 - RootMeanSquaredError: 0.4111\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.1124 - RootMeanSquaredError: 0.3352\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0462 - RootMeanSquaredError: 0.2150\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0378 - RootMeanSquaredError: 0.1944\n",
            "1/1 [==============================] - 0s 240ms/step - loss: 0.0339 - RootMeanSquaredError: 0.1841\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0289 - RootMeanSquaredError: 0.1701\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0263 - RootMeanSquaredError: 0.1621\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZt1F7yjgdAQ",
        "colab_type": "code",
        "outputId": "f5d1cd7f-1c21-43ff-a78f-952a981d679c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "picture_this_hist_all(rmses)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1jBs0iuNfO7/R9Uwa+VATRCMisuU16IzKzEaY2cdmNsPMfr2Rct8xMzezrzddiCIi0pLV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\nQmCC\n",
            "text/plain": [
              "<Figure size 504x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VgeUgL-Yys8",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}